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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Alternate conclusions on time variations of intensity in the inner radiation zone based on explorer vii data
Composition data obtained by the Neutral Atmosphere Composition Spectrometer carried aboard the Dynamics Explorer 2 satellite have been examined for variations in the polar regions during magnetically quiet conditions. A persistent enhancement of N2 density and a depletion of He density are present in the vicinity of the magnetic poles with maximum density response in the morning hours (magnetic) on average. A universal time (UT) variation in average density levels is evident near both the geographic and magnetic poles. There are systematic morphology changes with UT and between summer and winter which are qualitatively consistent with the shifting and spreading of thermospheric heating effects by the global circulation systems driven by EUV and magnetospheric sources. The magnitude of the UT variations is larger in the southern hemisphere on average, and the UT variations are larger in local winter than summer.
Herein this paper proposes an innovative approximate dynamic programming (ADP) method for distributed energy resource coordination with the loss of life of battery energy storage system (BESS) explicitly modeled. The dispatch policy is designed to account for both calendrical and cyclical aging effects on BESS, explicitly modeling the impacts of ambient temperature on BESS lifespan. The proposed ADP employs an adaptive critic method and enhanced off-policy deterministic policy gradient (DPG) strategy, addressing the limitations of the on-policy gradient-based ADP approaches, including inadequate exploration, low data usage, and computational complexity. In particular, a customized policy is proposed to guide the algorithm to explore some promising decisions and thereby improve exploration capability and learning efficiency compared to conventional DPG-based learning approaches, which may struggle to find a global optimum due to random noisy action-based exploration or require expert demonstration with extra effort. The proposed method is illustrated using the IEEE 123-node system and compared with the existing ADP methods to prove solution accuracy and demonstrate the effects of incorporating degradation models into control design. Case studies showed that the proposed ADP effectively coordinates DERs with a 10 times smaller optimization gap compared to existing methods, and the incorporation of the BESS life loss model ensures the expected lifespan and results in significant cost savings.
The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.
A clearly defined perpendicular fast reverse shock was found in the interplanetary plasma and magnetic field data from Explorers 33 and 35. An observation by Pioneer 8, which is not unambiguously identifiable because of insufficient data, indicates the evolution of this event. It is suggested that this reverse shock was formed from a stream-stream interaction and that an associated foward shock was developing and probably formed beyond the earth's orbit.
Establishing the Sun-Earth connection requires overcoming the challenges of exploring the data from past and current missions and leveraging tools and models (data mining) to create an efficient system treatment of the Sun and heliosphere. However, solar and heliospheric environment data constitute a vast source of information whose potential is far from being optimally exploited. In the next decade, the solar and heliospheric community will have to manage the increasing amount of information coming from new missions, improve reanalysis of data from past and current missions, and create new data products from the application of new methodologies. This complex task is further complicated by practical challenges such as different datasets and catalogs in different formats that may require different pre-processing and analysis tools, and the need for numerous analysis approaches that are not all fully optimized for large volumes of data. While several ongoing efforts aim at addressing these problems, the available datasets and tools are not always used to their full potential often due to lack of awareness of available resources. In this paper, we summarize the issues raised and goals discussed by members of the community during recent conference sessions focused on data mining for science.
The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.
Procedures for improving the modal modeling of structures using test data and to determine appropriate analytical models based on substructure experimental data were explored. Two related research topics were considered in modal modeling: using several independently acquired columns of frequency response data, and modal modeling using simultaneous multi-point excitation. In component mode synthesis modeling, the emphasis is on determining the best way to employ complex modes and residuals.
A multibeam microwave radar altimeter concept is numerically simulated to evaluate its capability to remotely sense mesoscale oceanographic features and eddies in particular. The study tests the sensitivity of the sensor to variations of systematic and environmental parameters, including sensor attitude angle, sensor position, and system errors. A novel concept of computing eddy vorticity from the multibeam data is explored. Application of this concept to the detection of simulated ocean eddies in the presence of tracker noise data gives excellent results; the technique is shown to be simple and accurate. The minimum size of the eddy detectable by the multibeam altimeter is presented for a given performance characteristic of the radar.
ABSTRACT Given the vast quantity of oil and gas input to the marine environment annually, hydrocarbon degradation by marine microorganisms is an essential ecosystem service. Linkages between taxonomy and hydrocarbon degradation capabilities are largely based on cultivation studies, leaving a knowledge gap regarding the intrinsic ability of uncultured marine microbes to degrade hydrocarbons. To address this knowledge gap, metagenomic sequence data from the Deepwater Horizon (DWH) oil spill deep-sea plume was assembled to which metagenomic and metatranscriptomic reads were mapped. Assembly and binning produced new DWH metagenome-assembled genomes that were evaluated along with their close relatives, all of which are from the marine environment (38 total). These analyses revealed globally distributed hydrocarbon-degrading microbes with clade-specific substrate degradation potentials that have not been reported previously. For example, methane oxidation capabilities were identified in all Cycloclasticus . Furthermore, all Bermanella encoded and expressed genes for non-gaseous n -alkane degradation; however, DWH Bermanella encoded alkane hydroxylase, not alkane 1-monooxygenase. All but one previously unrecognized DWH plume member in the SAR324 and UBA11654 have the capacity for aromatic hydrocarbon degradation. In contrast, Colwellia were diverse in the hydrocarbon substrates they could degrade. All clades encoded nutrient acquisition strategies and response to cold temperatures, while sensory and acquisition capabilities were clade specific. These novel insights regarding hydrocarbon degradation by uncultured planktonic microbes provides missing data, allowing for better prediction of the fate of oil and gas when hydrocarbons are input to the ocean, leading to a greater understanding of the ecological consequences to the marine environment. IMPORTANCE Microbial degradation of hydrocarbons is a critically important process promoting ecosystem health, yet much of what is known about this process is based on physiological experiments with a few hydrocarbon substrates and cultured microbes. Thus, the ability to degrade the diversity of hydrocarbons that comprise oil and gas by microbes in the environment, particularly in the ocean, is not well characterized. Therefore, this study aimed to utilize non-cultivation-based ‘omics data to explore novel genomes of uncultured marine microbes involved in degradation of oil and gas. Analyses of newly assembled metagenomic data and previously existing genomes from other marine data sets, with metagenomic and metatranscriptomic read recruitment, revealed globally distributed hydrocarbon-degrading marine microbes with clade-specific substrate degradation potentials that have not been previously reported. This new understanding of oil and gas degradation by uncultured marine microbes suggested that the global ocean harbors a diversity of hydrocarbon-degrading bacteria, which can act as primary agents regulating ecosystem health.
On the one hand, locating the right dataset, then figuring out how to use it, is a daunting task that is familiar to almost any scientist or graduate student in the fields of Earth system science. On the other hand, the ability to explore authentic Earth system science data, through inquiry-based education, is an important goal in US national education standards. Fortunately, in the digital age, tools are emerging that can make such data exploration commonplace at all educational levels. This paper describes the conception and development of one project that aims to bridge this gap: Mentoring and inquiry using NASA Data on Atmospheric and Earth science for Teachers and Amateurs (MY NASA DATA; mynasadata.larc.nasa.gov). With funding from NASA's Science Mission Directorate, this project was launched in early 2004 with the aim of developing microsets and identifying other enablers for making data accessible. A key feature of the project is a Live Access Server, the first educational implementation of this open source software, developed by NOAA, that makes it possible to explore multiple data formats through a single interface. This powerful tool is made more useful to the primary target audiences (K-12 and amateur scientists) through careful selection of the data offered, user-friendly explanations of the tool itself, and age-appropriate explanations of the parameters. However experience already shows that graduate students and even practicing scientists can also make use of this resource. The website also hosts teacher-contributed lesson plans, and seeks to feature reports of research projects that use the data.
A wind tunnel experiment simulating a steady three-dimensional helicopter rotor blade/vortex interaction is reported. The experimental configuration consisted of a vertical semispan vortex-generating wing, mounted upstream of a horizontal semispan rotor blade airfoil. A three-dimensional laser velocimeter was used to measure the velocity field in the region of the blade. Sectional lift coefficients were calculated by integrating the velocity field to obtain the bound vorticity. Total lift values, obtained by using an internal strain-gauge balance, verified the laser velocimeter data. Parametric variations of vortex strength, rotor blade angle of attack, and vortex position relative to the rotor blade were explored. These data are reported (with attention to experimental limitations) to provide a dataset for the validation of analytical work.
This talk will describe opportunities for translating research from open-road experiments with modified adaptive cruise controllers. The data and controllers used for the previous research are based on use cases and example drives from within the US. We explore processes, data sharing, experiments, and other techniques that we think will drive trans-Pacific partnerships with driving data.
We report the discovery of two ultra-faint stellar systems found in early data from the DECam Local Volume Exploration survey (DELVE). The first system, Centaurus I (DELVE J1238-4054), is identified as a resolved overdensity of old and metal-poor stars with a heliocentric distance of ${\rm D}_{\odot} = 116.3_{-0.6}^{+0.6}$ kpc, a half-light radius of $r_h = 2.3_{-0.3}^{+0.4}$ arcmin, an age of $\tau > 12.85$ Gyr, a metallicity of $Z = 0.0002_{-0.0002}^{+0.0001}$, and an absolute magnitude of $M_V = -5.55_{-0.11}^{+0.11}$ mag. This characterization is consistent with the population of ultra-faint satellites, and confirmation of this system would make Centaurus I one of the brightest recently discovered ultra-faint dwarf galaxies. Centaurus I is detected in Gaia DR2 with a clear and distinct proper motion signal, confirming that it is a real association of stars distinct from the Milky Way foreground; this is further supported by the clustering of blue horizontal branch stars near the centroid of the system. The second system, DELVE 1 (DELVE J1630-0058), is identified as a resolved overdensity of stars with a heliocentric distance of ${\rm D}_{\odot} = 19.0_{-0.6}^{+0.5} kpc$, a half-light radius of $r_h = 0.97_{-0.17}^{+0.24}$ arcmin, an age of $\tau = 12.5_{-0.7}^{+1.0}$ Gyr, a metallicity of $Z = 0.0005_{-0.0001}^{+0.0002}$, and an absolute magnitude of $M_V = -0.2_{-0.6}^{+0.8}$ mag, consistent with the known population of faint halo star clusters. Here, given the low number of probable member stars at magnitudes accessible with Gaia DR2, a proper motion signal for DELVE 1 is only marginally detected. We compare the spatial position and proper motion of both Centaurus I and DELVE 1 with simulations of the accreted satellite population of the Large Magellanic Cloud (LMC) and find that neither is likely to be associated with the LMC.
Multispectral data analysis was incorporated with multiseasonal data analysis based on a spectral radiance data-set. Concepts include comparing data types, exploring relationships between periodicity of LANDSAT and the seasonal sense of the Orientals, and derivation of a method to register the acquired data. Examples include a quality investigation of a paddy field by seasonal LANDSAT data, a progress check of the field in harvest season, and a detailed survey of the vegetational environment by summer and winter LANDSAT data.
Construction of a model of the regional magnetic field and investigation of the local magnetic anomalies and their origin were approaches used in attempts to study the crustal structure near Japan and its Antarctic bases. Spatial properties of the regional magnetic field and comparison of the regional model with that derived from MAGSAT data are discussed. Possible causes of the magnetic anomalies, and results of aeromagnetic surveys incorporating gravity and seismic data are explored. Ionospheric and magnetospheric contributions to geomagnetic variations, field-aligned currents, magnetic geomagnetic pulsations, and hydromagnetic waves by analysis of MAGSAT data are also examined.
Predicting molecular activity against protein targets is difficult because of the paucity of experimental data. Approaches like multitask modeling and collaborative filtering seek to improve model accuracy by leveraging results from multiple targets, but are limited because different compounds are measured with different assays, leading to sparse data matrices. Profile-QSAR (pQSAR) 2.0 addresses this problem by fitting a series of partial least squares models for each target, using as features the predictions from single-task models on the remaining targets. Here, this method has been shown to produce better results than single task and multitask models. However, the factors determining the success of pQSAR 2.0 have as yet not been characterized. In this paper we examine the experimental conditions that lead to better pQSAR models. We limit the amount of data available to the method by retraining with decreasing amounts of data and explore the model’s ability to generalize to compounds that have never been assayed. Finally, we look at the properties of training data needed to demonstrate pQSAR improvement.
Low energy electron distribution in outer portion of earth magnetosphere near geomagnetic equatorial plane - explorer satellite data